Computational approaches in antimicrobial drug discovery

This chapter explores the applications and potential of computational methods in antimicrobial drug discovery to address the escalating problem of antibiotic resistance. It begins by outlining the primary challenges in antimicrobial agent discovery, emphasizing the limitations of traditional drug development methods and the necessity of adopting computational techniques. The chapter then delves into the key applications of computer-aided drug design (CADD) in identifying novel antimicrobial compounds, utilizing advanced techniques such as molecular docking, pharmacophore modeling, quantitative structure-activity relationship analysis, and molecular dynamics simulations. In addition, it elucidates how artificial intelligence-driven drug discovery (AIDD) techniques, including machine learning and deep learning, enhance prediction accuracy and optimize drug design workflows. The chapter also discusses specific applications of AIDD and CADD in discovering antimethicillin-resistant Staphylococcus aureusdrugs, showcasing the advantages of computational methods in tackling real-world challenges. Finally, it evaluates the potential of computational tools in addressing current and emerging challenges in antimicrobial drug discovery, highlighting their critical role in rapidly developing effective novel therapies against evolving microbial threats. Overall, the chapter comprehensively introduces the extensive applications and immense prospects of computational methods in antimicrobial drug discovery, providing valuable insights and strategies to accelerate the research and development of new antibiotics.